Convalescent plasma for COVID-19 in hospitalised patients : an open-label, randomised clinical trial

Background: The effects of convalescent plasma (CP) therapy in hospitalised patients with coronavirus disease 2019 (COVID-19) remain uncertain. This study investigates the effect of CP on clinical improvement in these patients. Methods: This is an investigator-initiated, randomised, parallel arm, open-label, superiority clinical trial. Patients were randomly (1:1) assigned to two infusions of CP plus standard of care (SOC) or SOC alone. The primary outcome was the proportion of patients with clinical improvement 28 days after enrolment. Results: A total of 160 (80 in each arm) patients (66.3% critically ill, 33.7% severely ill) completed the trial. The median (interquartile range (IQR)) age was 60.5 (48–68) years; 58.1% were male and the median (IQR) time from symptom onset to randomisation was 10 (8–12) days. Neutralising antibody titres >1:80 were present in 133 (83.1%) patients at baseline. The proportion of patients with clinical improvement on day 28 was 61.3% in the CP+SOC group and 65.0% in the SOC group (difference −3.7%, 95% CI −18.8–11.3%). The results were similar in the severe and critically ill subgroups. There was no significant difference between CP+SOC and SOC groups in pre-specified secondary outcomes, including 28-day mortality, days alive and free of respiratory support and duration of invasive ventilatory support. Inflammatory and other laboratory marker values on days 3, 7 and 14 were similar between groups. Conclusions: CP+SOC did not result in a higher proportion of clinical improvement on day 28 in hospitalised patients with COVID-19 compared to SOC alone

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

PERCEPÇÃO SÓCIO-AMBIENTAL DAS MARISQUEIRAS NO MUNICÍPIO DE RAPOSA-MA

O município de Raposa tem uma área de 64,0 Km2 situada no quadrante nordeste da Ilha do Maranhão. A extração de marisco é uma atividade comum na região e serve como complemento na renda das famílias locais. A comunidade é composta quase que exclusivamente por mulheres, sendo a maioria na faixa etária dos 41 aos 50 anos. Grande parte é analfabeta, semi-analfabeta e apresentam renda inferior a um salário mínimo. Uma significativa parcela sobrevive exclusivamente da cata de mariscos, outra parcela tem nessa atividade apenas uma complementação em suas rendas e praticam outras atividades como: rendeiras, comerciantes, costureiras, entre outras. Desta forma conhecer a percepção ambiental das marisqueiras é de fundamental importância para análise da atividade, assim como serve de subsídio a um plano de manejo para conservação dos estoques naturais de mariscos presente na área

Limitação de esforço terapêutico na pessoa com lesão encefálica grave

As discussões a respeito das condutas de limitação de esforço terapêutico (LET) são frequentes nas unidades de terapia intensiva e na especialidade médica oncológica e são também importantes em contextos hospitalares de internação de longa permanência para vítimas de grandes traumas e agravos que necessitam de cuidados prolongados à saúde e de reinserção social. Na prática clínica, a tomada de decisão para LET é complexa e deve envolver o indivíduo, a família e a equipe multiprofissional. O objetivo deste artigo é discorrer a respeito da LET como um abrangente processo de "adequação de medidas" por agregação consensual de fatores centrado na pessoa, pautado por intensificação dos cuidados paliativos

Clinical characteristics and outcomes of hospital-manifested COVID-19 among Brazilians

ABSTRACT: Objectives: To analyze the clinical characteristics and outcomes of admitted patients with the hospital- versus community-manifested COVID-19 and to evaluate the risk factors related to mortality in the first population. Methods: This retrospective cohort included consecutive adult patients with COVID-19, hospitalized between March and September 2020. The demographic data, clinical characteristics, and outcomes were extracted from medical records. Patients with hospital-manifested COVID-19 (study group) and those with community-manifested COVID-19 (control group) were matched by the propensity score model. Logistic regression models were used to verify the risk factors for mortality in the study group. Results: Among 7,710 hospitalized patients who had COVID-19, 7.2% developed symptoms while admitted for other reasons. Patients with hospital-manifested COVID-19 had a higher prevalence of cancer (19.2% vs 10.8%) and alcoholism (8.8% vs 2.8%) than patients with community-manifested COVID-19 and also had a higher rate of intensive care unit requirement (45.1% vs 35.2%), sepsis (23.8% vs 14.5%), and death (35.8% vs 22.5%) (P <0.05 for all). The factors independently associated with increased mortality in the study group were increasing age, male sex, number of comorbidities, and cancer. Conclusion: Hospital-manifested COVID-19 was associated with increased mortality. Increasing age, male sex, number of comorbidities, and cancer were independent predictors of mortality among those with hospital-manifested COVID-19 disease

Development and validation of the MMCD score to predict kidney replacement therapy in COVID-19 patients

Abstract Background Acute kidney injury (AKI) is frequently associated with COVID-19, and the need for kidney replacement therapy (KRT) is considered an indicator of disease severity. This study aimed to develop a prognostic score for predicting the need for KRT in hospitalised COVID-19 patients, and to assess the incidence of AKI and KRT requirement. Methods This study is part of a multicentre cohort, the Brazilian COVID-19 Registry. A total of 5212 adult COVID-19 patients were included between March/2020 and September/2020. Variable selection was performed using generalised additive models (GAM), and least absolute shrinkage and selection operator (LASSO) regression was used for score derivation. Accuracy was assessed using the area under the receiver operating characteristic curve (AUC-ROC). Results The median age of the model-derivation cohort was 59 (IQR 47–70) years, 54.5% were men, 34.3% required ICU admission, 20.9% evolved with AKI, 9.3% required KRT, and 15.1% died during hospitalisation. The temporal validation cohort had similar age, sex, ICU admission, AKI, required KRT distribution and in-hospital mortality. The geographic validation cohort had similar age and sex; however, this cohort had higher rates of ICU admission, AKI, need for KRT and in-hospital mortality. Four predictors of the need for KRT were identified using GAM: need for mechanical ventilation, male sex, higher creatinine at hospital presentation and diabetes. The MMCD score had excellent discrimination in derivation (AUROC 0.929, 95% CI 0.918–0.939) and validation (temporal AUROC 0.927, 95% CI 0.911–0.941; geographic AUROC 0.819, 95% CI 0.792–0.845) cohorts and good overall performance (Brier score: 0.057, 0.056 and 0.122, respectively). The score is implemented in a freely available online risk calculator ( https://www.mmcdscore.com/ ). Conclusions The use of the MMCD score to predict the need for KRT may assist healthcare workers in identifying hospitalised COVID-19 patients who may require more intensive monitoring, and can be useful for resource allocation